A Relative Tendency Based Stock Market Prediction System

Abstract

Researchers have known for some time that non-linearity exists in the financial markets and that neural networks can be used to forecast market returns. In this article, we present a novel stock market prediction system which focuses on forecasting the relative tendency growth between different stocks and indices rather than purely predicting their values. This research utilizes artificial neural network models for estimation. The results are examined for their ability to provide an effective forecast of future values. Certain techniques, such as sliding windows and chaos theory, are employed for data preparation and pre-processing. Our system successfully predicted the relative tendency growth of different stocks with up to 99.01% accuracy.